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SQL-on-Hadoop tools help satisfy big data's elephantine appetite
Sponsored by IBM
This chapter is included in the Big data initiatives get huge boost from new technologies E-Book.
Despite all the attention it gets, Hadoop's use as a framework for supporting big data processing has been limited to programmers with specific skills. Enter SQL, the standard programming language for relational databases. The integration of SQL tools is speeding Hadoop's performance and opening the door to more developers and data analysts who are well-versed in SQL. Users can choose from more than a dozen SQL-on-Hadoop open source and commercial tools, but most of these technologies are immature and have kinks to work out. And since many are specialized, it's important to understand their optimal uses.
In this e-book chapter, Executive Editor Craig Stedman presents real-world examples of several organizations using SQL-on-Hadoop engines to simplify the process of querying and analyzing Hadoop data. He also delves into the IT challenges companies are facing and how they're resolving them. These businesses range from healthcare analytics providers to marketers to auto insurers to online dating services. Despite the diverse array of SQL-on-Hadoop users, one general theme prevails: Integrating SQL tools with Hadoop has definitely rejuvenated the elephant. One technical architect sums up SQL's influence this way: "Really all [the developers] understood was SQL. … So we were able to develop a lot more, a lot faster, because they were using the SQL syntax they were familiar with."
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